System

Abstract

To provide a system for efficiently collecting word-of-mouth information from various information sources and providing important information to a user.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects word-of-mouth information from various information sources. The analysis unit analyzes the word-of-mouth information collected by the collection unit and extracts important information. The summarizing unit summarizes the information extracted by the analyzing unit. The providing unit provides the information summarized by the summarizing unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to efficiently collect word-of-mouth information from various information sources, extract important information, and provide it to users.

[0005] The system according to the embodiment aims to efficiently collect word-of-mouth information from various information sources and provide important information to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects word-of-mouth information from various information sources. The analysis unit analyzes the word-of-mouth information collected by the collection unit and extracts important information. The summarization unit summarizes the information extracted by the analysis unit. The provision unit provides the information summarized by the summarization unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect word-of-mouth information from various information sources and provide important information to users. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An information aggregation system according to an embodiment of the present invention uses AI to aggregate word-of-mouth information from various sources, summarize it, and provide it to users. The AI ​​collects and analyzes word-of-mouth information from various sources, such as e-commerce sites, comparison sites, magazines, social media, and video sites (e.g., YouTube®), extracts important information, summarizes it, and provides it to users. For example, the information aggregation system collects product reviews from e-commerce sites, user posts from social media, and video comments from video sites. Next, the information aggregation system analyzes the collected word-of-mouth information using natural language processing technology to extract product ratings, user opinions, and common keywords. Furthermore, the information aggregation system summarizes the product's advantages and disadvantages, user evaluation trends, and other information based on the extracted information. Finally, the information aggregation system provides the summarized information to users, allowing them to efficiently obtain the information they need. This significantly reduces the user's burden and allows them to efficiently obtain the information they need. For example, users can avoid the hassle of individually checking multiple information sources, and users considering purchasing a product can quickly make purchasing decisions by referring to the word-of-mouth information summarized by AI.

[0029] An information aggregation system according to an embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects word-of-mouth information from various information sources. The various information sources include, but are not limited to, e-commerce sites, comparison sites, magazines, social networking sites, and video sites. The collection unit, for example, collects product reviews from e-commerce sites. The collection unit can also collect user posts from social networking sites. The collection unit can also collect video comments from video sites. For example, the collection unit automatically patrols reviews on e-commerce sites to obtain the latest word-of-mouth information. The analysis unit analyzes the word-of-mouth information collected by the collection unit and extracts important information. The analysis unit analyzes the word-of-mouth information using, for example, natural language processing technology. Examples of natural language processing technology include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit extracts, for example, product ratings, user opinions, and common keywords. The summarization unit summarizes the information extracted by the analysis unit. For example, the summarization unit summarizes the advantages and disadvantages of products, user evaluation trends, and the like. The summarizing unit summarizes important points for the user based on the extracted information, for example. The providing unit provides the information summarized by the summarizing unit to the user. The providing unit displays the summarized information on a web page, for example. The providing unit can also notify the user of the summarized information by email. As a result, the information aggregation system according to the embodiment significantly reduces the burden on the user and enables the user to efficiently obtain necessary information.

[0030] The collection unit can collect word-of-mouth information from information sources such as e-commerce sites, comparison sites, magazines, social networking sites, and video sites. For example, the collection unit collects product reviews on e-commerce sites. For example, the collection unit collects word-of-mouth information from e-commerce sites such as Yahoo! (registered trademark) Shopping and ZOZOTOWN (registered trademark). The collection unit can also collect comparative product reviews on comparison sites. For example, the collection unit collects word-of-mouth information from comparison sites such as Kakaku.com and TripAdvisor. The collection unit can also collect product review articles on magazines. For example, the collection unit collects word-of-mouth information from fashion magazines and business magazines. The collection unit can also collect user posts on social networking sites. For example, the collection unit collects word-of-mouth information from social networking sites such as Facebook (registered trademark), Twitter (registered trademark), and Instagram (registered trademark). The collection unit can also collect video comments on video sites. For example, the collection unit collects word-of-mouth information from comments on review videos and tutorial videos. This makes it possible to collect word-of-mouth information from a wide range of information sources.

[0031] The analysis unit can analyze the collected word-of-mouth information using natural language processing technology and extract important information. The analysis unit analyzes the word-of-mouth information using, for example, natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to these examples. The analysis unit extracts, for example, product ratings, user opinions, common keywords, etc. For example, the analysis unit can extract product rating scores. The analysis unit can also extract user opinions. The analysis unit can also extract common keywords. This makes it possible to efficiently extract important information from a large amount of word-of-mouth information.

[0032] The summarizing unit can summarize points that are important to the user based on the extracted information. The summarizing unit, for example, summarizes points that are important to the user based on the extracted information. The summarizing unit summarizes, for example, the advantages and disadvantages of a product, and trends in user evaluations. For example, the summarizing unit can summarize the advantages of a product. The summarizing unit can also summarize the disadvantages of a product. The summarizing unit can also summarize trends in user evaluations. This allows the user to grasp a large amount of word-of-mouth information at once.

[0033] The providing unit can provide the summarized information to the user. For example, the providing unit displays the summarized information on a web page. For example, the providing unit displays the summarized information on a web page so that the user can check it. The providing unit can also notify the user of the summarized information by email. For example, the providing unit notifies the user of the summarized information by email so that the user can check it. This allows the user to efficiently obtain the information they need.

[0034] The collection unit can analyze the user's past review browsing history and select the optimal collection method. For example, the collection unit prioritizes collecting information sources that the user has frequently visited in the past. For example, the collection unit prioritizes collecting review information that the user has given a high rating in the past. For example, the collection unit prioritizes collecting review information from information sources that the user has visited for a long time in the past. This makes it possible to select the optimal collection method based on the user's past browsing history. Analysis of the past review browsing history is performed using data such as pages viewed and viewing time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's browsing history data into a generation AI and cause the generation AI to select the optimal collection method.

[0035] When collecting word-of-mouth information, the collection unit can filter the word-of-mouth information based on the user's current areas of interest. For example, the collection unit prioritizes collecting word-of-mouth information about products in which the user is currently interested. For example, the collection unit collects word-of-mouth information related to keywords recently searched by the user. For example, the collection unit collects word-of-mouth information about products mentioned by influencers followed by the user. This allows the word-of-mouth information to be filtered based on the user's current areas of interest. The current areas of interest are identified using data such as recent search history and browsing history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's search history data into a generation AI and have the generation AI identify the user's areas of interest.

[0036] When collecting word-of-mouth information, the collection unit can evaluate the reliability of the information source and prioritize collecting highly reliable information. The collection unit, for example, prioritizes collecting word-of-mouth information from highly reliable e-commerce sites and comparison sites. The collection unit, for example, prioritizes collecting word-of-mouth information from highly reliable users and reviewers. The collection unit, for example, prioritizes collecting word-of-mouth information from highly reliable media and magazines. This allows for the preferential collection of highly reliable information, thereby providing highly reliable word-of-mouth information. The reliability of the information source is evaluated using, for example, the evaluation score of the information source or past reliability data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the reliability data of the information source into the generation AI and cause the generation AI to evaluate the reliability.

[0037] When collecting word-of-mouth information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting word-of-mouth information about the area where the user is currently located. For example, the collection unit collects word-of-mouth information related to places the user has visited in the past. For example, the collection unit prioritizes collecting word-of-mouth information about travel destinations the user is planning. This makes it possible to collect highly relevant word-of-mouth information based on the user's geographical location information. The geographical location information is acquired using, for example, GPS data or an IP address. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information data into a generation AI and cause the generation AI to identify highly relevant information.

[0038] When collecting word-of-mouth information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit collects word-of-mouth information about brands and products that the user follows on social media. For example, the collection unit collects word-of-mouth information related to posts that the user has "liked" or shared on social media. For example, the collection unit collects word-of-mouth information related to groups and communities that the user participates in on social media. This makes it possible to collect related word-of-mouth information based on the user's social media activity. The analysis of social media activity is performed using data such as the content of posts, the number of "likes," and the number of followers. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to identify related information.

[0039] When collecting word-of-mouth information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially uses collection methods that the user has previously rated highly. For example, the collection unit avoids collection methods that the user has previously expressed dissatisfaction with. For example, the collection unit optimizes the collection method based on feedback the user has previously provided. This allows the collection method to be customized based on the user's past feedback. Analysis of past feedback is performed using data such as evaluation comments and star ratings. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and have the generation AI customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the word-of-mouth information. For example, the analysis unit performs a detailed analysis of word-of-mouth information with a high level of importance. For example, the analysis unit performs a simplified analysis of word-of-mouth information with a low level of importance. For example, the analysis unit determines the priority of the analysis according to the importance. This allows the level of detail of the analysis to be adjusted based on the importance of the word-of-mouth information. The importance of the word-of-mouth information is evaluated using, for example, an evaluation score or frequently occurring keywords. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the word-of-mouth information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of word-of-mouth information. For example, the analysis unit uses different analysis algorithms for product reviews and service reviews. For example, the analysis unit uses different analysis algorithms for social media posts and comments on video sites. For example, the analysis unit uses different analysis algorithms for magazine articles and reviews on e-commerce sites. This allows the application of the optimal analysis algorithm depending on the category of word-of-mouth information. The categories of word-of-mouth information are classified using, for example, product categories or service categories. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the word-of-mouth information into the generation AI and cause the generation AI to apply the appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm by referring to analysis results that the user has previously rated highly. The analysis unit, for example, avoids analysis results that the user has previously expressed dissatisfaction with. The analysis unit, for example, improves the accuracy of the analysis based on the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. The use of past analysis results is performed using data such as past analysis reports and evaluation results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the posting time of the review information. The analysis unit, for example, prioritizes analysis of the most recent review information. The analysis unit, for example, references past review information to determine the priority of analysis. The analysis unit, for example, prioritizes analysis of review information posted within a specific period. This allows the priority of analysis to be determined based on the posting time of the review information. The posting time of the review information is evaluated using, for example, the most recent post or past posts. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the posting time of the review information into the generation AI and cause the generation AI to determine the priority of analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the word-of-mouth information. For example, the analysis unit prioritizes analysis of highly relevant word-of-mouth information. For example, the analysis unit postpones analysis of less relevant word-of-mouth information. For example, the analysis unit determines the order of analysis according to the relevance. This allows the order of analysis to be adjusted based on the relevance of the word-of-mouth information. The relevance of word-of-mouth information is evaluated using, for example, the same topic or the same category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of word-of-mouth information to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit provides analysis results that use a lot of technical terms to a user with high level of expertise. For example, the analysis unit provides analysis results that avoid technical terms to a user with low level of expertise. For example, the analysis unit adjusts the way in which the analysis results are expressed according to the user's level of expertise. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise. The user's level of expertise is evaluated using, for example, questionnaire results or past behavioral history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terms.

[0046] The summarization unit can adjust the level of detail of the summary based on the importance of the word-of-mouth information when generating a summary. For example, the summarization unit provides a detailed summary for word-of-mouth information with a high level of importance. For example, the summarization unit provides a simplified summary for word-of-mouth information with a low level of importance. For example, the summarization unit determines the priority of the summary according to the importance. This allows the level of detail of the summary to be adjusted based on the importance of the word-of-mouth information. The importance of the word-of-mouth information is evaluated using, for example, an evaluation score or frequently occurring keywords. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input importance data of the word-of-mouth information to the generation AI and cause the generation AI to adjust the level of detail of the summary.

[0047] When generating summaries, the summarization unit can apply different summarization algorithms depending on the category of word-of-mouth information. For example, the summarization unit uses different summarization algorithms for product reviews and service reviews. For example, the summarization unit uses different summarization algorithms for social media posts and comments on video sites. For example, the summarization unit uses different summarization algorithms for magazine articles and reviews on e-commerce sites. This allows the application of the optimal summarization algorithm depending on the category of word-of-mouth information. The categories of word-of-mouth information are classified using, for example, product categories or service categories. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input category data of word-of-mouth information into the generation AI and cause the generation AI to apply the appropriate summarization algorithm.

[0048] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. The summarization unit, for example, adjusts the summarization algorithm by referring to summary results that the user has previously rated highly. The summarization unit, for example, avoids summary results that the user has previously expressed dissatisfaction with. The summarization unit, for example, improves the accuracy of the summary based on the user's past summarization results. This allows the accuracy of the summary to be improved by referring to the user's past summarization results. The use of past summarization results is performed using data such as past summary reports and evaluation results. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without AI. For example, the summarization unit can input the user's past summary result data into the generation AI and cause the generation AI to improve the accuracy of the summary.

[0049] When generating summaries, the summarizing unit can determine the priority of summaries based on the posting time of the word-of-mouth information. For example, the summarizing unit prioritizes summarizing the most recent word-of-mouth information. For example, the summarizing unit determines the priority of summaries by referring to past word-of-mouth information. For example, the summarizing unit prioritizes summarizing word-of-mouth information posted within a specific period. This allows the priority of summaries to be determined based on the posting time of the word-of-mouth information. The posting time of the word-of-mouth information is evaluated using, for example, the most recent post or past posts. Some or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input data on the posting time of the word-of-mouth information into the generating AI and have the generating AI determine the priority of summaries.

[0050] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the word-of-mouth information. For example, the summarization unit prioritizes summarization of highly relevant word-of-mouth information. For example, the summarization unit postpones summarization of less relevant word-of-mouth information. For example, the summarization unit determines the order of summaries according to the relevance. This allows the order of summaries to be adjusted based on the relevance of the word-of-mouth information. The relevance of word-of-mouth information is evaluated using, for example, the same topic or the same category. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input relevance data of word-of-mouth information to the generation AI and have the generation AI adjust the order of summaries.

[0051] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, the summarization unit provides a summary that uses a lot of technical terms to a user with high expertise. For example, the summarization unit provides a summary that avoids technical terms to a user with low expertise. For example, the summarization unit adjusts the way the summary is expressed according to the user's level of expertise. This allows the use of technical terms in the summary to be adjusted according to the user's level of expertise. The user's level of expertise is evaluated using, for example, survey results or past behavioral history. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terms.

[0052] The providing unit can select the optimal display method by referring to the user's past browsing history when providing the data. For example, the providing unit preferentially uses a display method that the user has previously preferred. For example, the providing unit avoids a display method that the user has previously expressed dissatisfaction with. For example, the providing unit selects the optimal display method based on the user's past browsing history. This allows the optimal display method to be selected based on the user's past browsing history. Analysis of the past browsing history is performed using data such as viewed pages and viewing time. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's browsing history data to the generation AI and cause the generation AI to select the optimal display method.

[0053] The providing unit can customize the display content according to the user's current task at the time of providing. For example, if the user is considering purchasing a product, the providing unit prioritizes displaying related word-of-mouth information. For example, if the user is planning a trip, the providing unit prioritizes displaying travel-related word-of-mouth information. For example, if the user is attending a specific event, the providing unit prioritizes displaying word-of-mouth information related to the event. This allows the display content to be customized according to the user's current task. The current task is identified using data such as current work content and goals. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generating AI and cause the generating AI to customize the display content.

[0054] The providing unit can improve the display method by reflecting user feedback when providing the display. The providing unit, for example, optimizes the display method based on feedback previously provided by the user. The providing unit, for example, avoids a display method that the user has expressed dissatisfaction with. The providing unit, for example, improves the display method by reflecting user feedback. This allows the display method to be improved based on user feedback. Feedback analysis is performed using data such as evaluation comments and star ratings. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the display method.

[0055] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. For example, if the user is using a tablet, the providing unit provides a display method that is optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides a display method that is simple and highly visible. This allows the optimal display method to be selected based on the user's device information. The device information is acquired using data such as the device type, screen size, and OS. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0056] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit provides a language switching function, for example, when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the display content in that language. This makes it possible to make the display content multilingual according to the user's language setting. The language setting is acquired using data such as the language used and regional setting. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data to the generation AI and cause the generation AI to execute multilingual settings.

[0057] The providing unit can provide highly relevant information preferentially, taking into consideration the user's geographical location information. For example, the providing unit can provide word-of-mouth information about the area where the user is currently located preferentially. For example, the providing unit can provide word-of-mouth information related to places the user has visited in the past. For example, the providing unit can provide word-of-mouth information about travel destinations the user is planning. This makes it possible to provide highly relevant information based on the user's geographical location information. The geographical location information is acquired using, for example, GPS data or an IP address. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's location information data to the generation AI and cause the generation AI to identify highly relevant information.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can analyze the user's past review browsing history and select the optimal collection method. For example, it can prioritize collecting information sources that the user has frequently visited in the past. It can prioritize collecting review information that the user has given high ratings in the past. It can prioritize collecting review information from information sources that the user has visited for a long time in the past. This allows the optimal collection method to be selected based on the user's past browsing history. Analysis of the past review browsing history is performed using data such as pages viewed and viewing time. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's browsing history data into the generation AI and have the generation AI select the optimal collection method.

[0060] When collecting review information, the collection unit can filter the review information based on the user's current areas of interest. For example, the collection unit can prioritize collecting review information about products in which the user is currently interested. Collect review information related to keywords recently searched by the user. Collect review information about products mentioned by influencers the user follows. This allows the review information to be filtered based on the user's current areas of interest. The current areas of interest are identified using data such as recent search history and browsing history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's search history data into the generation AI and have the generation AI identify the areas of interest.

[0061] When collecting word-of-mouth information, the collection unit can evaluate the reliability of the information source and prioritize collecting highly reliable information. For example, the collection unit can prioritize collecting word-of-mouth information from highly reliable e-commerce sites and comparison sites. The collection unit can prioritize collecting word-of-mouth information from highly reliable users and reviewers. The collection unit can prioritize collecting word-of-mouth information from highly reliable media and magazines. This allows the collection of highly reliable information to be prioritized, thereby providing highly reliable word-of-mouth information. The reliability of the information source is evaluated using the evaluation score of the information source, past reliability data, etc. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the reliability data of the information source into the generation AI and have the generation AI perform the reliability evaluation.

[0062] When collecting review information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting review information for the area where the user is currently located. The collection unit can collect review information related to places the user has visited in the past. The collection unit can prioritize collecting review information for travel destinations the user is planning. This makes it possible to collect highly relevant review information based on the user's geographical location information. The geographical location information is acquired using GPS data, IP addresses, etc. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's location information data into the generation AI and cause the generation AI to identify highly relevant information.

[0063] When collecting review information, the collection unit can analyze the user's social media activity and collect related information. For example, it can collect review information about brands and products that the user follows on social media. It can collect review information related to posts that the user has "liked" or shared on social media. It can collect review information related to groups and communities that the user participates in on social media. This makes it possible to collect related review information based on the user's social media activity. The analysis of social media activity is performed using data such as the content of posts, the number of "likes," and the number of followers. Some or all of the above-mentioned processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI identify related information.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects word-of-mouth information from various sources. Specifically, information is collected from e-commerce sites, comparison sites, magazines, social media, video sites, etc. For example, product reviews are collected from e-commerce sites, user posts are collected from social media, and video comments are collected from video sites. The collection unit automatically patrols these sources and obtains the latest word-of-mouth information. Step 2: The analysis unit analyzes the word-of-mouth information collected by the collection unit and extracts important information. The analysis unit uses natural language processing technology to analyze the word-of-mouth information and extract product ratings, user opinions, common keywords, etc. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. Step 3: The summarization section summarizes the information extracted by the analysis section. The summarization section summarizes the advantages and disadvantages of the product, trends in user evaluations, and other important points for users. Step 4: The providing unit provides the user with the information summarized by the summarizing unit. The providing unit can display the summarized information on a web page or notify the user by email.

[0066] (Example 2) An information aggregation system according to an embodiment of the present invention uses AI to aggregate word-of-mouth information from various sources, summarize it, and provide it to users. The AI ​​collects and analyzes word-of-mouth information from various sources, such as e-commerce sites, comparison sites, magazines, social media, and video sites (e.g., YouTube), extracts important information, summarizes it, and provides it to users. For example, the information aggregation system collects product reviews from e-commerce sites, user posts from social media, and video comments from video sites. Next, the information aggregation system analyzes the collected word-of-mouth information using natural language processing technology to extract product ratings, user opinions, and common keywords. Furthermore, based on the extracted information, the information aggregation system summarizes the product's advantages and disadvantages, user evaluation trends, and other information. Finally, the information aggregation system provides the summarized information to users, allowing them to efficiently obtain the information they need. This significantly reduces the user's burden and allows them to efficiently obtain the information they need. For example, users can avoid the hassle of individually checking multiple information sources, and users considering purchasing a product can quickly make purchasing decisions by referring to the word-of-mouth information summarized by AI.

[0067] An information aggregation system according to an embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects word-of-mouth information from various information sources. The various information sources include, but are not limited to, e-commerce sites, comparison sites, magazines, social networking sites, and video sites. The collection unit, for example, collects product reviews from e-commerce sites. The collection unit can also collect user posts from social networking sites. The collection unit can also collect video comments from video sites. For example, the collection unit automatically patrols reviews on e-commerce sites to obtain the latest word-of-mouth information. The analysis unit analyzes the word-of-mouth information collected by the collection unit and extracts important information. The analysis unit analyzes the word-of-mouth information using, for example, natural language processing technology. Examples of natural language processing technology include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit extracts, for example, product ratings, user opinions, and common keywords. The summarization unit summarizes the information extracted by the analysis unit. For example, the summarization unit summarizes the advantages and disadvantages of products, user evaluation trends, and the like. The summarizing unit summarizes important points for the user based on the extracted information, for example. The providing unit provides the information summarized by the summarizing unit to the user. The providing unit displays the summarized information on a web page, for example. The providing unit can also notify the user of the summarized information by email. As a result, the information aggregation system according to the embodiment significantly reduces the burden on the user and enables the user to efficiently obtain necessary information.

[0068] The collection unit can collect word-of-mouth information from sources such as e-commerce sites, comparison sites, magazines, social media, and video sites. For example, the collection unit collects product reviews on e-commerce sites. For example, the collection unit collects word-of-mouth information from e-commerce sites such as Yahoo! Shopping and ZOZOTOWN. The collection unit can also collect comparative product reviews on comparison sites. For example, the collection unit collects word-of-mouth information from comparison sites such as Kakaku.com and TripAdvisor. The collection unit can also collect product review articles on magazines. For example, the collection unit collects word-of-mouth information from fashion magazines and business magazines. The collection unit can also collect user posts on social media. For example, the collection unit collects word-of-mouth information from social media such as Facebook, Twitter, and Instagram. The collection unit can also collect video comments on video sites. For example, the collection unit collects word-of-mouth information from comments on review videos and tutorial videos. This makes it possible to collect word-of-mouth information from a wide range of sources.

[0069] The analysis unit can analyze the collected word-of-mouth information using natural language processing technology and extract important information. The analysis unit analyzes the word-of-mouth information using, for example, natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to these examples. The analysis unit extracts, for example, product ratings, user opinions, common keywords, etc. For example, the analysis unit can extract product rating scores. The analysis unit can also extract user opinions. The analysis unit can also extract common keywords. This makes it possible to efficiently extract important information from a large amount of word-of-mouth information.

[0070] The summarizing unit can summarize points that are important to the user based on the extracted information. The summarizing unit, for example, summarizes points that are important to the user based on the extracted information. The summarizing unit summarizes, for example, the advantages and disadvantages of a product, and trends in user evaluations. For example, the summarizing unit can summarize the advantages of a product. The summarizing unit can also summarize the disadvantages of a product. The summarizing unit can also summarize trends in user evaluations. This allows the user to grasp a large amount of word-of-mouth information at once.

[0071] The providing unit can provide the summarized information to the user. For example, the providing unit displays the summarized information on a web page. For example, the providing unit displays the summarized information on a web page so that the user can check it. The providing unit can also notify the user of the summarized information by email. For example, the providing unit notifies the user of the summarized information by email so that the user can check it. This allows the user to efficiently obtain the information they need.

[0072] The collection unit can estimate the user's emotions and adjust the timing of collecting word-of-mouth information based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and starts collection when the user is relaxed. For example, if the user is excited, the collection unit immediately collects word-of-mouth information and provides it quickly. For example, if the user is tired, the collection unit adjusts the collection timing and starts collection after the user has rested. This allows word-of-mouth information to be collected at the optimal timing depending on the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0073] The collection unit can analyze the user's past review browsing history and select the optimal collection method. For example, the collection unit prioritizes collecting information sources that the user has frequently visited in the past. For example, the collection unit prioritizes collecting review information that the user has given a high rating in the past. For example, the collection unit prioritizes collecting review information from information sources that the user has visited for a long time in the past. This makes it possible to select the optimal collection method based on the user's past browsing history. Analysis of the past review browsing history is performed using data such as pages viewed and viewing time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's browsing history data into a generation AI and cause the generation AI to select the optimal collection method.

[0074] When collecting word-of-mouth information, the collection unit can filter the word-of-mouth information based on the user's current areas of interest. For example, the collection unit prioritizes collecting word-of-mouth information about products in which the user is currently interested. For example, the collection unit collects word-of-mouth information related to keywords recently searched by the user. For example, the collection unit collects word-of-mouth information about products mentioned by influencers followed by the user. This allows the word-of-mouth information to be filtered based on the user's current areas of interest. The current areas of interest are identified using data such as recent search history and browsing history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's search history data into a generation AI and have the generation AI identify the user's areas of interest.

[0075] When collecting word-of-mouth information, the collection unit can evaluate the reliability of the information source and prioritize collecting highly reliable information. The collection unit, for example, prioritizes collecting word-of-mouth information from highly reliable e-commerce sites and comparison sites. The collection unit, for example, prioritizes collecting word-of-mouth information from highly reliable users and reviewers. The collection unit, for example, prioritizes collecting word-of-mouth information from highly reliable media and magazines. This allows for the preferential collection of highly reliable information, thereby providing highly reliable word-of-mouth information. The reliability of the information source is evaluated using, for example, the evaluation score of the information source or past reliability data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the reliability data of the information source into the generation AI and cause the generation AI to evaluate the reliability.

[0076] The collection unit can estimate the user's emotions and determine the priority of the word-of-mouth information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting positive word-of-mouth information. For example, when the user is relaxed, the collection unit also collects negative word-of-mouth information. For example, when the user is excited, the collection unit prioritizes collecting the latest word-of-mouth information. This allows the priority of word-of-mouth information to be determined according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotions.

[0077] When collecting word-of-mouth information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting word-of-mouth information about the area where the user is currently located. For example, the collection unit collects word-of-mouth information related to places the user has visited in the past. For example, the collection unit prioritizes collecting word-of-mouth information about travel destinations the user is planning. This makes it possible to collect highly relevant word-of-mouth information based on the user's geographical location information. The geographical location information is acquired using, for example, GPS data or an IP address. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information data into a generation AI and cause the generation AI to identify highly relevant information.

[0078] When collecting word-of-mouth information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit collects word-of-mouth information about brands and products that the user follows on social media. For example, the collection unit collects word-of-mouth information related to posts that the user has "liked" or shared on social media. For example, the collection unit collects word-of-mouth information related to groups and communities that the user participates in on social media. This makes it possible to collect related word-of-mouth information based on the user's social media activity. The analysis of social media activity is performed using data such as the content of posts, the number of "likes," and the number of followers. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to identify related information.

[0079] When collecting word-of-mouth information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially uses collection methods that the user has previously rated highly. For example, the collection unit avoids collection methods that the user has previously expressed dissatisfaction with. For example, the collection unit optimizes the collection method based on feedback the user has previously provided. This allows the collection method to be customized based on the user's past feedback. Analysis of past feedback is performed using data such as evaluation comments and star ratings. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and have the generation AI customize the collection method.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows the way the analysis is presented to be adjusted according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the word-of-mouth information. For example, the analysis unit performs a detailed analysis of word-of-mouth information with a high level of importance. For example, the analysis unit performs a simplified analysis of word-of-mouth information with a low level of importance. For example, the analysis unit determines the priority of the analysis according to the importance. This allows the level of detail of the analysis to be adjusted based on the importance of the word-of-mouth information. The importance of the word-of-mouth information is evaluated using, for example, an evaluation score or frequently occurring keywords. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the word-of-mouth information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of word-of-mouth information. For example, the analysis unit uses different analysis algorithms for product reviews and service reviews. For example, the analysis unit uses different analysis algorithms for social media posts and comments on video sites. For example, the analysis unit uses different analysis algorithms for magazine articles and reviews on e-commerce sites. This allows the application of the optimal analysis algorithm depending on the category of word-of-mouth information. The categories of word-of-mouth information are classified using, for example, product categories or service categories. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the word-of-mouth information into the generation AI and cause the generation AI to apply the appropriate analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm by referring to analysis results that the user has previously rated highly. The analysis unit, for example, avoids analysis results that the user has previously expressed dissatisfaction with. The analysis unit, for example, improves the accuracy of the analysis based on the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. The use of past analysis results is performed using data such as past analysis reports and evaluation results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the posting time of the review information. The analysis unit, for example, prioritizes analysis of the most recent review information. The analysis unit, for example, references past review information to determine the priority of analysis. The analysis unit, for example, prioritizes analysis of review information posted within a specific period. This allows the priority of analysis to be determined based on the posting time of the review information. The posting time of the review information is evaluated using, for example, the most recent post or past posts. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the posting time of the review information into the generation AI and cause the generation AI to determine the priority of analysis.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the word-of-mouth information. For example, the analysis unit prioritizes analysis of highly relevant word-of-mouth information. For example, the analysis unit postpones analysis of less relevant word-of-mouth information. For example, the analysis unit determines the order of analysis according to the relevance. This allows the order of analysis to be adjusted based on the relevance of the word-of-mouth information. The relevance of word-of-mouth information is evaluated using, for example, the same topic or the same category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of word-of-mouth information to the generation AI and cause the generation AI to adjust the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit provides analysis results that use a lot of technical terms to a user with high level of expertise. For example, the analysis unit provides analysis results that avoid technical terms to a user with low level of expertise. For example, the analysis unit adjusts the way in which the analysis results are expressed according to the user's level of expertise. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise. The user's level of expertise is evaluated using, for example, questionnaire results or past behavioral history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terms.

[0088] The summarization unit can estimate the user's emotions and adjust the presentation method of the summary based on the estimated user's emotions. For example, if the user is nervous, the summarization unit provides a simple, highly visible summary. For example, if the user is relaxed, the summarization unit provides a detailed summary. For example, if the user is excited, the summarization unit provides a visually stimulating summary. This allows the presentation method of the summary to be adjusted according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0089] The summarization unit can adjust the level of detail of the summary based on the importance of the word-of-mouth information when generating a summary. For example, the summarization unit provides a detailed summary for word-of-mouth information with a high level of importance. For example, the summarization unit provides a simplified summary for word-of-mouth information with a low level of importance. For example, the summarization unit determines the priority of the summary according to the importance. This allows the level of detail of the summary to be adjusted based on the importance of the word-of-mouth information. The importance of the word-of-mouth information is evaluated using, for example, an evaluation score or frequently occurring keywords. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input importance data of the word-of-mouth information to the generation AI and cause the generation AI to adjust the level of detail of the summary.

[0090] When generating summaries, the summarization unit can apply different summarization algorithms depending on the category of the word-of-mouth information. For example, the summarization unit uses different summarization algorithms for product reviews and service reviews. For example, the summarization unit uses different summarization algorithms for social media posts and YouTube comments. For example, the summarization unit uses different summarization algorithms for magazine articles and e-commerce site reviews. This allows the application of the optimal summarization algorithm depending on the category of the word-of-mouth information. The category of word-of-mouth information is classified using, for example, product categories or service categories. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input category data of the word-of-mouth information into the generation AI and cause the generation AI to apply the appropriate summarization algorithm.

[0091] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. The summarization unit, for example, adjusts the summarization algorithm by referring to summary results that the user has previously rated highly. The summarization unit, for example, avoids summary results that the user has previously expressed dissatisfaction with. The summarization unit, for example, improves the accuracy of the summary based on the user's past summarization results. This allows the accuracy of the summary to be improved by referring to the user's past summarization results. The use of past summarization results is performed using data such as past summary reports and evaluation results. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without AI. For example, the summarization unit can input the user's past summary result data into the generation AI and cause the generation AI to improve the accuracy of the summary.

[0092] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated user's emotions. For example, if the user is in a hurry, the summarization unit provides a short, to-the-point summary. For example, if the user is relaxed, the summarization unit provides a detailed summary. For example, if the user is excited, the summarization unit provides a visually stimulating summary. This allows the length of the summary to be adjusted according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0093] When generating summaries, the summarizing unit can determine the priority of summaries based on the posting time of the word-of-mouth information. For example, the summarizing unit prioritizes summarizing the most recent word-of-mouth information. For example, the summarizing unit determines the priority of summaries by referring to past word-of-mouth information. For example, the summarizing unit prioritizes summarizing word-of-mouth information posted within a specific period. This allows the priority of summaries to be determined based on the posting time of the word-of-mouth information. The posting time of the word-of-mouth information is evaluated using, for example, the most recent post or past posts. Some or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input data on the posting time of the word-of-mouth information into the generating AI and have the generating AI determine the priority of summaries.

[0094] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the word-of-mouth information. For example, the summarization unit prioritizes summarization of highly relevant word-of-mouth information. For example, the summarization unit postpones summarization of less relevant word-of-mouth information. For example, the summarization unit determines the order of summaries according to the relevance. This allows the order of summaries to be adjusted based on the relevance of the word-of-mouth information. The relevance of word-of-mouth information is evaluated using, for example, the same topic or the same category. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input relevance data of word-of-mouth information to the generation AI and have the generation AI adjust the order of summaries.

[0095] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, the summarization unit provides a summary that uses a lot of technical terms to a user with high expertise. For example, the summarization unit provides a summary that avoids technical terms to a user with low expertise. For example, the summarization unit adjusts the way the summary is expressed according to the user's level of expertise. This allows the use of technical terms in the summary to be adjusted according to the user's level of expertise. The user's level of expertise is evaluated using, for example, survey results or past behavioral history. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terms.

[0096] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. For example, if the user is relaxed, the providing unit provides a display method including detailed information. For example, if the user is excited, the providing unit provides a visually stimulating display method. This makes it possible to adjust the display method of the information to be provided according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0097] The providing unit can select the optimal display method by referring to the user's past browsing history when providing the data. For example, the providing unit preferentially uses a display method that the user has previously preferred. For example, the providing unit avoids a display method that the user has previously expressed dissatisfaction with. For example, the providing unit selects the optimal display method based on the user's past browsing history. This allows the optimal display method to be selected based on the user's past browsing history. Analysis of the past browsing history is performed using data such as viewed pages and viewing time. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's browsing history data to the generation AI and cause the generation AI to select the optimal display method.

[0098] The providing unit can customize the display content according to the user's current task at the time of providing. For example, if the user is considering purchasing a product, the providing unit prioritizes displaying related word-of-mouth information. For example, if the user is planning a trip, the providing unit prioritizes displaying travel-related word-of-mouth information. For example, if the user is attending a specific event, the providing unit prioritizes displaying word-of-mouth information related to the event. This allows the display content to be customized according to the user's current task. The current task is identified using data such as current work content and goals. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generating AI and cause the generating AI to customize the display content.

[0099] The providing unit can improve the display method by reflecting user feedback when providing the display. The providing unit, for example, optimizes the display method based on feedback previously provided by the user. The providing unit, for example, avoids a display method that the user has expressed dissatisfaction with. The providing unit, for example, improves the display method by reflecting user feedback. This allows the display method to be improved based on user feedback. Feedback analysis is performed using data such as evaluation comments and star ratings. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the display method.

[0100] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit prioritizes providing positive word-of-mouth information. For example, if the user is relaxed, the providing unit also provides negative word-of-mouth information. For example, if the user is excited, the providing unit prioritizes providing the latest word-of-mouth information. This makes it possible to determine the priority of information to be provided according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotion.

[0101] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. For example, if the user is using a tablet, the providing unit provides a display method that is optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides a display method that is simple and highly visible. This allows the optimal display method to be selected based on the user's device information. The device information is acquired using data such as the device type, screen size, and OS. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0102] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit provides a language switching function, for example, when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the display content in that language. This makes it possible to make the display content multilingual according to the user's language setting. The language setting is acquired using data such as the language used and regional setting. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data to the generation AI and cause the generation AI to execute multilingual settings.

[0103] The providing unit can provide highly relevant information preferentially, taking into consideration the user's geographical location information. For example, the providing unit can provide word-of-mouth information about the area where the user is currently located preferentially. For example, the providing unit can provide word-of-mouth information related to places the user has visited in the past. For example, the providing unit can provide word-of-mouth information about travel destinations the user is planning. This makes it possible to provide highly relevant information based on the user's geographical location information. The geographical location information is acquired using, for example, GPS data or an IP address. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's location information data to the generation AI and cause the generation AI to identify highly relevant information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, summarization unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects word-of-mouth information using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A executes the collection process. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the word-of-mouth information using natural language processing technology. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the extracted information. The provision unit provides the summarized information to the user, for example, by the output device 40 of the smart device 14. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting word-of-mouth information based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, summarization unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects word-of-mouth information using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A executes the collection process. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the word-of-mouth information using natural language processing technology. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the extracted information. The provision unit provides the summarized information to the user, for example, by the speaker 240 of the smart glasses 214. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting word-of-mouth information based on the estimated user emotions. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, summarization unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects word-of-mouth information using the camera 42 and microphone 238 of the headset-type terminal 314, and the control unit 46A executes the collection process. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the word-of-mouth information using natural language processing technology. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the extracted information. The provision unit provides the summarized information to the user, for example, by the display 343 of the headset-type terminal 314. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting word-of-mouth information based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, summarization unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects word-of-mouth information using the camera 42 and microphone 238 of the robot 414, and the control unit 46A executes the collection process. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the word-of-mouth information using natural language processing technology. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the extracted information. The provision unit provides the summarized information to the user, for example, by the speaker 240 of the robot 414. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting word-of-mouth information based on the estimated user emotions.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, positive word-of-mouth information is prioritized for analysis. If the user is relaxed, negative word-of-mouth information is also analyzed. If the user is excited, the latest word-of-mouth information is prioritized for analysis. This allows the analysis priority to be determined according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0106] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is excited, a visually stimulating display method is provided. This makes it possible to adjust the display method of the information to be provided according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's facial expression data into the generating AI and have the generating AI perform emotion estimation.

[0107] The collection unit can estimate the user's emotions and determine the priority of the word-of-mouth information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, positive word-of-mouth information is collected first. If the user is relaxed, negative word-of-mouth information is also collected. If the user is excited, the latest word-of-mouth information is collected first. This allows the priority of word-of-mouth information to be determined according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0108] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible summary is provided. If the user is relaxed, a detailed summary is provided. If the user is excited, a visually stimulating summary is provided. This allows the way the summary is presented to be adjusted according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the summarization unit may be performed using AI, or may be performed without using AI. For example, the summarization unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0109] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, a short and to-the-point analysis result is provided. If the user is relaxed, a detailed analysis result is provided. If the user is excited, a visually stimulating analysis result is provided. This allows the length of the analysis to be adjusted according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0110] The collection unit can analyze the user's past review browsing history and select the optimal collection method. For example, it can prioritize collecting information sources that the user has frequently visited in the past. It can prioritize collecting review information that the user has given high ratings in the past. It can prioritize collecting review information from information sources that the user has visited for a long time in the past. This allows the optimal collection method to be selected based on the user's past browsing history. Analysis of the past review browsing history is performed using data such as pages viewed and viewing time. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's browsing history data into the generation AI and have the generation AI select the optimal collection method.

[0111] When collecting review information, the collection unit can filter the review information based on the user's current areas of interest. For example, the collection unit can prioritize collecting review information about products in which the user is currently interested. Collect review information related to keywords recently searched by the user. Collect review information about products mentioned by influencers the user follows. This allows the review information to be filtered based on the user's current areas of interest. The current areas of interest are identified using data such as recent search history and browsing history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's search history data into the generation AI and have the generation AI identify the areas of interest.

[0112] When collecting word-of-mouth information, the collection unit can evaluate the reliability of the information source and prioritize collecting highly reliable information. For example, the collection unit can prioritize collecting word-of-mouth information from highly reliable e-commerce sites and comparison sites. The collection unit can prioritize collecting word-of-mouth information from highly reliable users and reviewers. The collection unit can prioritize collecting word-of-mouth information from highly reliable media and magazines. This allows the collection of highly reliable information to be prioritized, thereby providing highly reliable word-of-mouth information. The reliability of the information source is evaluated using the evaluation score of the information source, past reliability data, etc. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the reliability data of the information source into the generation AI and have the generation AI perform the reliability evaluation.

[0113] When collecting review information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting review information for the area where the user is currently located. The collection unit can collect review information related to places the user has visited in the past. The collection unit can prioritize collecting review information for travel destinations the user is planning. This makes it possible to collect highly relevant review information based on the user's geographical location information. The geographical location information is acquired using GPS data, IP addresses, etc. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's location information data into the generation AI and cause the generation AI to identify highly relevant information.

[0114] When collecting review information, the collection unit can analyze the user's social media activity and collect related information. For example, it can collect review information about brands and products that the user follows on social media. It can collect review information related to posts that the user has "liked" or shared on social media. It can collect review information related to groups and communities that the user participates in on social media. This makes it possible to collect related review information based on the user's social media activity. The analysis of social media activity is performed using data such as the content of posts, the number of "likes," and the number of followers. Some or all of the above-mentioned processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI identify related information.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects word-of-mouth information from various sources. Specifically, information is collected from e-commerce sites, comparison sites, magazines, social media, video sites, etc. For example, product reviews are collected from e-commerce sites, user posts are collected from social media, and video comments are collected from video sites. The collection unit automatically patrols these sources and obtains the latest word-of-mouth information. Step 2: The analysis unit analyzes the word-of-mouth information collected by the collection unit and extracts important information. The analysis unit uses natural language processing technology to analyze the word-of-mouth information and extract product ratings, user opinions, common keywords, etc. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. Step 3: The summarization section summarizes the information extracted by the analysis section. The summarization section summarizes the advantages and disadvantages of the product, trends in user evaluations, and other important points for users. Step 4: The providing unit provides the user with the information summarized by the summarizing unit. The providing unit can display the summarized information on a web page or notify the user by email.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects word-of-mouth information from various sources, an analysis unit that analyzes the word-of-mouth information collected by the collection unit and extracts important information; a summarizing unit that summarizes the information extracted by the analyzing unit; a providing unit that provides the information summarized by the summarizing unit to a user. A system characterized by:

2. The collecting unit Collect word-of-mouth information from sources such as e-commerce sites, comparison sites, magazines, social media, and video sites.

2. The system of claim 1.

3. The analysis unit Analyze collected reviews using natural language processing technology to extract important information 2. The system of claim 1.

4. The summary section Summarize the key points for the user based on the extracted information 2. The system of claim 1.

5. The providing unit Providing summarized information to users 2. The system of claim 1.

6. The collecting unit Estimate user emotions and adjust the timing of word-of-mouth information collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze users' past review browsing history and select the appropriate collection method 2. The system of claim 1.

8. The collecting unit When collecting reviews, filter them based on the user's current interests.

2. The system of claim 1.

Images